{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ibmqfactory.load_account:WARNING:2020-09-10 23:03:16,788: Credentials are already in use. The existing account in the session will be replaced.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# Importing standard Qiskit libraries and configuring account\n",
    "from qiskit import QuantumCircuit, execute, Aer, IBMQ\n",
    "from qiskit.compiler import transpile, assemble\n",
    "from qiskit.tools.jupyter import *\n",
    "from qiskit.visualization import *\n",
    "# Loading your IBM Q account(s)\n",
    "provider = IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 10 - Aer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<QasmSimulator('qasm_simulator') from AerProvider()>,\n",
       " <StatevectorSimulator('statevector_simulator') from AerProvider()>,\n",
       " <UnitarySimulator('unitary_simulator') from AerProvider()>,\n",
       " <PulseSimulator('pulse_simulator') from AerProvider()>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View all available Aer backends\n",
    "Aer.backends()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<QasmSimulatorPy('qasm_simulator') from BasicAer()>,\n",
       " <StatevectorSimulatorPy('statevector_simulator') from BasicAer()>,\n",
       " <UnitarySimulatorPy('unitary_simulator') from BasicAer()>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import BasicAer\n",
    "BasicAer.backends() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<IBMQSimulator('ibmq_qasm_simulator') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmqx2') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_16_melbourne') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_vigo') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_ourense') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_valencia') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_london') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_burlington') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_essex') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_armonk') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_santiago') from IBMQ(hub='ibm-q', group='open', project='main')>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View all available IBMQ backends\n",
    "provider.backends()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<IBMQSimulator('ibmq_qasm_simulator') from IBMQ(hub='ibm-q', group='open', project='main')>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View all IBMQ provider simulators only\n",
    "provider.backends(simulator=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7efe608a1750>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a quantum circuit\n",
    "qc = QuantumCircuit(2, 2)\n",
    "qc.h(0)\n",
    "qc.cx(0, 1)\n",
    "qc.measure([0, 1], [0, 1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'00': 526, '11': 498}\n"
     ]
    }
   ],
   "source": [
    "# Import the QasmSimulator from Aer provider\n",
    "from qiskit.providers.aer import QasmSimulator\n",
    "backend_simulator = QasmSimulator()\n",
    "# Set the backend options, method set to statevector\n",
    "options = {'method': 'statevector'}\n",
    "# Execute circuit using the backend options created\n",
    "job = execute(qc, backend_simulator, backend_options=options)\n",
    "# Print out the result counts\n",
    "result = job.result()\n",
    "counts = result.get_counts(qc)\n",
    "print(counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the Qasm simulator and set the backend options\n",
    "aer_qasm_simulator = Aer.get_backend('qasm_simulator')\n",
    "options = {'method': 'statevector'}\n",
    "# Execute the circuit with the Aer Qasm simulator\n",
    "job = execute(qc, aer_qasm_simulator, backend_options=options)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory results:  ['11', '00', '00', '11', '00', '00', '11', '00', '00', '11']\n"
     ]
    }
   ],
   "source": [
    "# Set the backend options, method set to statevector\n",
    "options = {'method': 'statevector', 'memory':True, 'shots':10}\n",
    "# Execute circuit using the backend options created\n",
    "job = execute(qc, backend_simulator, backend_options=options)\n",
    "result = job.result()\n",
    "# Pull the memory slots for the circuit\n",
    "memory = result.get_memory(qc)\n",
    "# Print the results from the memory slots\n",
    "print('Memory results: ', memory)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory results:  ['11', '00', '11', '11', '00']\n"
     ]
    }
   ],
   "source": [
    "# View each measurement individually by enabling the memory parameter\n",
    "aer_backend = Aer.get_backend('qasm_simulator')\n",
    "# Set backend, shots, and memory parameters and retrieve results\n",
    "result = execute(qc, backend=aer_backend, shots=5, memory=True).result()\n",
    "\n",
    "# Pull the memory slots results\n",
    "memory = result.get_memory(qc)\n",
    "\n",
    "# Print the memory slots\n",
    "print('Memory results: ', memory)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 381.432x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct a 2 qubit quantum circuit\n",
    "qc_init = QuantumCircuit(2, 2)\n",
    "# Import numpy to simplify some math for us\n",
    "import numpy as np\n",
    "# Select the qubits by their index which you wish to initialize\n",
    "init_qubits = [0, 1]\n",
    "# Inititialize qubit states\n",
    "qc_init.initialize([1, 0, 0, 1] / np.sqrt(2), init_qubits)\n",
    "# Add measurements and draw the initialized circuit\n",
    "qc_init.measure(range(2), range(2))\n",
    "qc_init.decompose()\n",
    "qc_init.draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['11', '00', '11', '00', '11', '11', '11', '00', '00', '00']\n"
     ]
    }
   ],
   "source": [
    "# Set the memory to True so we can observe each result\n",
    "result = execute(qc_init, aer_backend, shots=10, memory=True).result()\n",
    "# Retrieve the individual results from the memory slots\n",
    "memory = result.get_memory(qc_init)\n",
    "# Print the memory slots\n",
    "print(memory)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 622.377x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a 4 qubit circuit\n",
    "qc_init2 = QuantumCircuit(4, 4)\n",
    "\n",
    "# Import numpy to help with some arithmetic\n",
    "import numpy as np\n",
    "# Initialize only the last 3 qubits\n",
    "initialized_qubits = [1, 2, 3]\n",
    "\n",
    "# Set the initial state, remember that the sum of amplitudes-squared \n",
    "# must equal 1\n",
    "qc_init2.initialize([0, 1, 0, 1, 0, 1, 0, 1] / np.sqrt(4), initialized_qubits)\n",
    "\n",
    "# Add a barrier so it is easier to read\n",
    "qc_init2.barrier(range(4))\n",
    "\n",
    "# Measure qubits, decompose and draw circuit\n",
    "qc_init2.measure(range(4), range(4))\n",
    "qc_init2.decompose()\n",
    "qc_init2.draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'0010': 277, '0110': 244, '1010': 267, '1110': 236}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute the circuit and print results and histogram\n",
    "result = execute(qc_init2, backend_simulator).result()\n",
    "counts = result.get_counts(qc_init2)\n",
    "print(counts)\n",
    "plot_histogram(counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 682.577x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a 4-qubit circuit\n",
    "qc_init_x = QuantumCircuit(4, 4)\n",
    "# Import numpy \n",
    "import numpy as np\n",
    "# Initialize the last 3 qubits, same as before\n",
    "initialized_qubits = [1, 2, 3]\n",
    "qc_init_x.initialize([0, 1, 0, 1, 0, 1, 0, 1] / np.sqrt(4), initialized_qubits)\n",
    "\n",
    "# Add a barrier so it is easier to read\n",
    "qc_init_x.barrier(range(4))\n",
    "# Include an X gate on all qubits\n",
    "for idx in range(4):\n",
    "    qc_init_x.x(idx)\n",
    "# Measure and draw the circuit\n",
    "qc_init_x.measure(range(4), range(4))\n",
    "qc_init_x.decompose()\n",
    "qc_init_x.draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'0001': 243, '0101': 276, '1001': 238, '1101': 267}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute and get counts\n",
    "result = execute(qc_init_x, backend_simulator).result()\n",
    "counts = result.get_counts(qc_init_x)\n",
    "print(counts)\n",
    "plot_histogram(counts)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running on a statevector simulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 187.706x84.28 with 1 Axes>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct quantum circuit\n",
    "qc = QuantumCircuit(1)\n",
    "# Place qubit in superposition\n",
    "qc.h(0)\n",
    "qc.draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.70710678+0.j, 0.70710678+0.j])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the Statevector simulator from the Aer provider\n",
    "simulator = Aer.get_backend('statevector_simulator')\n",
    "# Execute the circuit\n",
    "result = execute(qc, simulator).result()\n",
    "# Get the state vector and display the results\n",
    "statevector = result.get_statevector(qc)\n",
    "statevector\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 200.832x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct quantum circuit \n",
    "qc = QuantumCircuit(2)\n",
    "# Place both in superposition\n",
    "qc.h(0)\n",
    "qc.h(1)\n",
    "qc.draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5+0.j, 0.5+0.j, 0.5+0.j, 0.5+0.j])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute the circuit using the state vector simulator\n",
    "result = execute(qc, simulator).result()\n",
    "# Extract the state vector of the circuit from the results\n",
    "statevector = result.get_statevector(qc)\n",
    "# Output the state vector values\n",
    "statevector\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.70710678+0.j, 0.        +0.j, 0.        +0.j, 0.70710678+0.j])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct quantum circuit\n",
    "qc = QuantumCircuit(2)\n",
    "# Place the first qubit in superposition\n",
    "qc.h(0)\n",
    "# Entangle the two qubits together using a CNOT gate, \n",
    "# where the first is the control and the second qubit is the target.\n",
    "qc.cx(0, 1)\n",
    "# Execute the circuit on the state vector simulator\n",
    "result = execute(qc, simulator).result()\n",
    "# Obtain the state vector of the circuit\n",
    "statevector = result.get_statevector(qc)\n",
    "# Output the state vector values\n",
    "statevector\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display state vector\n",
    "plot_state_city(statevector)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the qsphere class\n",
    "from qiskit.visualization import plot_state_qsphere\n",
    "%matplotlib inline\n",
    "# Create quantum circuit\n",
    "qc = QuantumCircuit(1)\n",
    "# Place the qubit in a superposition state\n",
    "qc.h(0)\n",
    "# Execute the circuit on the statevector simulator\n",
    "backend = Aer.get_backend('statevector_simulator')\n",
    "job = execute(qc, simulator).result()\n",
    "# Display the QSphere with result\n",
    "plot_state_qsphere(statevector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a quantum circuit\n",
    "qc = QuantumCircuit(1)\n",
    "# Rotate the state from |0 to |1 by applying an X gate\n",
    "qc.x(0)\n",
    "# Place qubit in a superposition from the |1 state\n",
    "qc.h(0)\n",
    "# Execute the circuit on the state vector simulator\n",
    "job = execute(qc, simulator).result()\n",
    "# Plot the results onto the QSphere\n",
    "plot_state_qsphere(job.get_statevector(qc))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a quantum circuit\n",
    "qc = QuantumCircuit(1)\n",
    "# Place qubit in a superposition from the |0 state\n",
    "qc.h(0)\n",
    "# Apply a Z (phase) gate, to rotate it by an angle  around the Z axis\n",
    "qc.z(0)\n",
    "# Execute the circuit on the state vector simulator\n",
    "job = execute(qc, simulator).result()\n",
    "# Plot the results onto the QSphere\n",
    "plot_state_qsphere(job.get_statevector(qc))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running on a Unitary simulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unitary of the circuit:\n",
      " [[ 0.70710678+0.00000000e+00j  0.70710678-8.65956056e-17j]\n",
      " [ 0.70710678+0.00000000e+00j -0.70710678+8.65956056e-17j]]\n"
     ]
    }
   ],
   "source": [
    "# Create a quantum circuit and add a Hadamard gate\n",
    "qc = QuantumCircuit(1)\n",
    "qc.h(0)\n",
    "\n",
    "# Set the simulator to the UnitarySimulator from the Aer provider\n",
    "simulator = Aer.get_backend('unitary_simulator')\n",
    "\n",
    "# Execute the circuit on the unitary simulator\n",
    "result = execute(qc, simulator).result()\n",
    "# Extract the unitary matrix from the results\n",
    "unitary = result.get_unitary(qc)\n",
    "# Print out the unitary matrix\n",
    "print(\"Unitary of the circuit:\\n\", unitary)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unitary of the circuit:\n",
      " [[ 0.70710678+0.00000000e+00j  0.70710678-8.65956056e-17j]\n",
      " [-0.70710678+0.00000000e+00j  0.70710678-8.65956056e-17j]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 247.906x84.28 with 1 Axes>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new circuit, adding an H gate followed by a Z gate\n",
    "qc = QuantumCircuit(1)\n",
    "qc.h(0)\n",
    "qc.z(0)\n",
    "\n",
    "# Execute the circuit on the unitary simulator\n",
    "result = execute(qc, simulator).result()\n",
    "# Retrieve the unitary matrix from the results\n",
    "unitary = result.get_unitary(qc)\n",
    "# Print the unitary matrix\n",
    "print(\"Unitary of the circuit:\\n\", unitary)\n",
    "qc.draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unitary of the circuit:\n",
      " [[ 0.70710678+0.j  0.70710678+0.j]\n",
      " [-0.70710678+0.j  0.70710678+0.j]]\n"
     ]
    }
   ],
   "source": [
    "# Create a quantum circuit\n",
    "qc_init = QuantumCircuit(1)\n",
    "# Set the initial unitary using the result from the previous example.\n",
    "opts = {\"initial_unitary\": np.array([[ 1,  1],\n",
    "                                     [-1, 1]]/np.sqrt(2))}\n",
    "\n",
    "# Execute and obtain Unitary matrix of the circuit\n",
    "result = execute(qc_init, simulator, backend_options=opts).result()\n",
    "# Retrieve the unitary matrix from the result\n",
    "unitary = result.get_unitary(qc_init)\n",
    "# Print the unitary matrix results\n",
    "print(\"Unitary of the circuit:\\n\", unitary)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running on a Pulse simulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/qiskit/compiler/assemble.py:323: RuntimeWarning: Dynamic rep rates not supported on this backend. rep_time will be used instead of rep_delay.\n",
      "  RuntimeWarning,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pulse Qobj: 1622a073-0855-4987-b957-d5343424f8f9:\n",
      "Config: {'init_qubits': True,\n",
      " 'meas_level': 2,\n",
      " 'meas_lo_freq': [inf],\n",
      " 'meas_return': 'avg',\n",
      " 'memory': False,\n",
      " 'memory_slot_size': 100,\n",
      " 'memory_slots': 1,\n",
      " 'parametric_pulses': [],\n",
      " 'pulse_library': [{'name': '2f613389417215751c42c82118155cdf2abc51325c3c997a5c836d970b0ddd64',\n",
      "                    'samples': array([ 0.00000000e+00+0.j,  9.95678466e-02+0.j,  1.98146143e-01+0.j,\n",
      "        2.94755174e-01+0.j,  3.88434796e-01+0.j,  4.78253979e-01+0.j,\n",
      "        5.63320058e-01+0.j,  6.42787610e-01+0.j,  7.15866849e-01+0.j,\n",
      "        7.81831482e-01+0.j,  8.40025923e-01+0.j,  8.89871809e-01+0.j,\n",
      "        9.30873749e-01+0.j,  9.62624247e-01+0.j,  9.84807753e-01+0.j,\n",
      "        9.97203797e-01+0.j,  9.99689182e-01+0.j,  9.92239207e-01+0.j,\n",
      "        9.74927912e-01+0.j,  9.47927346e-01+0.j,  9.11505852e-01+0.j,\n",
      "        8.66025404e-01+0.j,  8.11938006e-01+0.j,  7.49781203e-01+0.j,\n",
      "        6.80172738e-01+0.j,  6.03804410e-01+0.j,  5.21435203e-01+0.j,\n",
      "        4.33883739e-01+0.j,  3.42020143e-01+0.j,  2.46757398e-01+0.j,\n",
      "        1.49042266e-01+0.j,  4.98458857e-02+0.j, -4.98458857e-02+0.j,\n",
      "       -1.49042266e-01+0.j, -2.46757398e-01+0.j, -3.42020143e-01+0.j,\n",
      "       -4.33883739e-01+0.j, -5.21435203e-01+0.j, -6.03804410e-01+0.j,\n",
      "       -6.80172738e-01+0.j, -7.49781203e-01+0.j, -8.11938006e-01+0.j,\n",
      "       -8.66025404e-01+0.j, -9.11505852e-01+0.j, -9.47927346e-01+0.j,\n",
      "       -9.74927912e-01+0.j, -9.92239207e-01+0.j, -9.99689182e-01+0.j,\n",
      "       -9.97203797e-01+0.j, -9.84807753e-01+0.j, -9.62624247e-01+0.j,\n",
      "       -9.30873749e-01+0.j, -8.89871809e-01+0.j, -8.40025923e-01+0.j,\n",
      "       -7.81831482e-01+0.j, -7.15866849e-01+0.j, -6.42787610e-01+0.j,\n",
      "       -5.63320058e-01+0.j, -4.78253979e-01+0.j, -3.88434796e-01+0.j,\n",
      "       -2.94755174e-01+0.j, -1.98146143e-01+0.j, -9.95678466e-02+0.j,\n",
      "       -2.44929360e-16+0.j])}],\n",
      " 'qubit_lo_freq': [inf],\n",
      " 'shots': 1024}\n",
      "Header: {'backend_name': 'pulse_simulator', 'backend_version': '0.6.1'}\n",
      "Experiments:\n",
      "\n",
      "Pulse Experiment:\n",
      "Header:\n",
      "{'memory_slots': 1, 'name': 'pulse_sample_schedule'}\n",
      "Config:\n",
      "{}\n",
      "\n",
      "\tInstruction: 2f613389417215751c42c82118155cdf2abc51325c3c997a5c836d970b0ddd64\n",
      "\t\tt0: 0\n",
      "\t\tch: d0\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Import the PulseSystemModel\n",
    "from qiskit.providers.aer.pulse import PulseSystemModel\n",
    "# Import Pulse classes needed to generate a schedule\n",
    "from qiskit.pulse import Play, DriveChannel\n",
    "from qiskit.pulse import Schedule, Waveform\n",
    "\n",
    "# Import numpy and generate the sin sample values\n",
    "import numpy as np\n",
    "x = np.linspace(0,2*np.pi,64)\n",
    "data = np.sin(x)\n",
    "# Generate a SamplePulse \n",
    "sample_pulse = Waveform(data, name=\"sin_64_pulse\") \n",
    "# Create a schedule\n",
    "schedules = Schedule(name='pulse_sample_schedule')\n",
    "# Operate on the first qubit\n",
    "qubit_idx = 0\n",
    "# Insert the sample pulse\n",
    "schedules = schedules.insert(0, Play(sample_pulse, DriveChannel(qubit_idx)))\n",
    "\n",
    "# Instantiate the PulseSimulator\n",
    "from qiskit.providers.aer import PulseSimulator\n",
    "backend_sim = PulseSimulator()\n",
    "\n",
    "\n",
    "# Assemble schedules using PulseSimulator as the backend\n",
    "pulse_qobj = assemble(schedules, backend=backend_sim)\n",
    "# Set the system model by replicating the ibmq_armonk backend\n",
    "armonk_backend = provider.get_backend('ibmq_armonk')\n",
    "system_model = PulseSystemModel.from_backend(armonk_backend)\n",
    "\n",
    "# Run simulation on a PulseSystemModel object and print results\n",
    "results = backend_sim.run(pulse_qobj, system_model)\n",
    "print(results.qobj())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating Noise models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.tools.visualization import plot_histogram\n",
    "# Create a 2-qubit circuit \n",
    "qc = QuantumCircuit(2, 2)\n",
    "# Add some arbitrary gates and measurement operators\n",
    "qc.h(0)\n",
    "qc.cx(0, 1)\n",
    "qc.measure([0, 1], [0, 1])\n",
    "\n",
    "# Execute the circuit on the qasm simulator\n",
    "result = execute(qc, Aer.get_backend('qasm_simulator')).result()\n",
    "# Obtain and print results \n",
    "counts = result.get_counts()\n",
    "plot_histogram(counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute the same circuit on a real quantum computer\n",
    "result = execute(qc, provider.get_backend('ibmq_valencia')).result()\n",
    "# Obtain and print results\n",
    "counts = result.get_counts()\n",
    "plot_histogram(counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set backend:  ibmqx2\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1bbda23eede9434ca64b858d7bb27dd7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(VBox(layout=Layout(max_width='710px', min_width='710px')),), layout=Layout(max_height='500…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div.job_widget')\n",
       "        .detach()\n",
       "        .appendTo($('#header'))\n",
       "        .css({\n",
       "            'z-index': 999,\n",
       "             'position': 'fixed',\n",
       "            'box-shadow': '5px 5px 5px -3px black',\n",
       "            'opacity': 0.95,\n",
       "            'float': 'left,'\n",
       "        })\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qiskit.providers.ibmq import least_busy\n",
    "\n",
    "backend = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= (num_qubits) and\n",
    "                                   not x.configuration().simulator and x.status().operational==True))\n",
    "print(\"Set backend: \", backend)\n",
    "\n",
    "# Launch the job watcher widget\n",
    "%qiskit_job_watcher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the NoiseModel\n",
    "from qiskit.providers.aer.noise import NoiseModel\n",
    "\n",
    "# Create the noise model based on the backend properties\n",
    "noise_model = NoiseModel.from_backend(backend)\n",
    "\n",
    "# Get coupling map from backend\n",
    "coupling_map = backend.configuration().coupling_map\n",
    "\n",
    "# Get basis gates from noise model\n",
    "basis_gates = noise_model.basis_gates\n",
    "\n",
    "# Execute the circuit on the simulator with the backend properties, \n",
    "# and generated noise model\n",
    "result = execute(qc, Aer.get_backend('qasm_simulator'),\n",
    "                 coupling_map=coupling_map,\n",
    "                 basis_gates=basis_gates,\n",
    "                 noise_model=noise_model).result()\n",
    "# Obtain and print results\n",
    "counts = result.get_counts()\n",
    "plot_histogram(counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NoiseModel:\n",
      "  Basis gates: ['cx', 'id', 'u1', 'u2', 'u3']\n",
      "  Instructions with noise: ['u1', 'u2', 'u3', 'id']\n",
      "  All-qubits errors: ['id', 'u1', 'u2', 'u3']\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize your T1 and T2 values\n",
    "t1 = 0.0125  \n",
    "t2 = 0.0025   \n",
    "\n",
    "# Apply the T1 and T2 to create the thermal relaxation error\n",
    "from qiskit.providers.aer.noise import thermal_relaxation_error\n",
    "t_error = thermal_relaxation_error(t1, t2, 0.01)\n",
    "\n",
    "# Add the errors to a noise model \n",
    "# and apply to all basis gates on all qubits\n",
    "noise_model = NoiseModel()\n",
    "noise_model.add_all_qubit_quantum_error(t_error, ['id', 'u1', 'u2', 'u3'])\n",
    "# Print out the noise model \n",
    "print(noise_model) \n",
    "\n",
    "#Create the same 2-qubit quantum circuit as before\n",
    "qc_error = QuantumCircuit(2,2)\n",
    "qc_error.h(0)\n",
    "qc_error.cx(0,1)\n",
    "qc_error.measure(range(2), range(2))\n",
    "\n",
    "# Set the simulator \n",
    "simulator = Aer.get_backend('qasm_simulator')\n",
    "# Apply the noise model we created to the execution method\n",
    "result = execute(qc_error, simulator, shots=1024, basis_gates=noise_model.basis_gates, noise_model=noise_model).result()\n",
    "\n",
    "# Obtain results and print\n",
    "counts = result.get_counts(qc_error)\n",
    "plot_histogram(counts)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build your own noise model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the error classes and methods\n",
    "from qiskit.providers.aer.noise import depolarizing_error\n",
    "from qiskit.providers.aer.noise import ReadoutError\n",
    "\n",
    "# Single and multi-qubit probability error\n",
    "single_qubit_gate_p = 0.25  \n",
    "multi_qubit_gate_p = 0.1   \n",
    "\n",
    "# Apply the depolarizing quantum errors\n",
    "single_error = depolarizing_error(single_qubit_gate_p, 1)\n",
    "multi_error = depolarizing_error(multi_qubit_gate_p, 2)   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NoiseModel:\n",
      "  Basis gates: ['cx', 'id', 'u2', 'u3']\n",
      "  Instructions with noise: ['cx', 'u2']\n",
      "  All-qubits errors: ['u2', 'cx']\n"
     ]
    }
   ],
   "source": [
    "# Add the single and multi-qubit errors to the noise model\n",
    "noise_model = NoiseModel()\n",
    "noise_model.add_all_qubit_quantum_error(single_error, ['u2'])\n",
    "noise_model.add_all_qubit_quantum_error(multi_error, ['cx'])\n",
    "\n",
    "# Print out the noise model\n",
    "print(noise_model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NoiseModel:\n",
      "  Basis gates: ['cx', 'id', 'u2', 'u3']\n",
      "  Instructions with noise: ['cx', 'u2', 'measure']\n",
      "  Qubits with noise: [0]\n",
      "  All-qubits errors: ['u2', 'cx']\n",
      "  Specific qubit errors: [('measure', [0])]\n"
     ]
    }
   ],
   "source": [
    "# Set the readout error probabilities for 0 given 1, & 1 given 0,\n",
    "p0_1 = 0.7\n",
    "p1_0 = 0.2\n",
    "p0 = 1 - p0_1\n",
    "p1 = 1 - p1_0\n",
    "\n",
    "# Construct the ReadoutError with the probabilities\n",
    "readout_error = ReadoutError([[p0, p0_1], [p1_0, p1]])\n",
    "# Apply the readout error to qubit 0. \n",
    "noise_model.add_readout_error(readout_error, [0])\n",
    "# Print the noise model\n",
    "print(noise_model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7efd7b4612d0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a simple 2 qubit circuit\n",
    "qc_error = QuantumCircuit(2,2)\n",
    "qc_error.h(0)\n",
    "qc_error.cx(0,1)\n",
    "qc_error.measure(range(2), range(2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the Qasm Simulator\n",
    "simulator = Aer.get_backend('qasm_simulator')\n",
    "# Set the backend parameters, including our noise model, and execute\n",
    "result = execute(qc_error, simulator, shots=1024, basis_gates=noise_model.basis_gates, noise_model=noise_model).result()\n",
    "# Obtain the result counts and print\n",
    "counts = result.get_counts(qc_error)\n",
    "plot_histogram(counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.20.1</td></tr><tr><td>Terra</td><td>0.15.2</td></tr><tr><td>Aer</td><td>0.6.1</td></tr><tr><td>Ignis</td><td>0.4.0</td></tr><tr><td>Aqua</td><td>0.7.5</td></tr><tr><td>IBM Q Provider</td><td>0.8.0</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.8 | packaged by conda-forge | (default, Jul 31 2020, 02:25:08) \n",
       "[GCC 7.5.0]</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>31.40900421142578</td></tr><tr><td colspan='2'>Thu Sep 10 23:28:36 2020 UTC</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
